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CS 552/652 Speech Recognition with Hidden Markov Models Winter 2011

CS 552/652 Speech Recognition with Hidden Markov Models Winter 2011

CS 552/652 Speech Recognition with Hidden Markov Models Winter 2011 Oregon Health & Science University Center for Spoken Language Understanding John-Paul Hosom Lecture 12 February 16 Expectation Maximization, Embedded Training. Project 3: Forward-Backward Algorithm.

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540 views • 40 slides
Chapter 11: Sampling Methods (Section 1,2,3)

Chapter 11: Sampling Methods (Section 1,2,3)

Chapter 11: Sampling Methods (Section 1,2,3). Shiqi Zhang Machine Learning Class Instructor: Dr. Sridharan Mohan Nov 2, 2009. Link to Importance Sampling Example : A Paper from Dieter Fox.

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556 views • 30 slides
Integrating Constraints and Metric Learning in Semi-Supervised Clustering

Integrating Constraints and Metric Learning in Semi-Supervised Clustering

Integrating Constraints and Metric Learning in Semi-Supervised Clustering. Mikhail Bilenko, Sugato Basu, Raymond J. Mooney ICML 2004 Presented by Xin Li. Semi-Supervised Clustering. K=4. Semi-Supervised Clustering. Semi-Supervised Clustering. How to exploit supervision in clustering.

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385 views • 21 slides
Integrating Constraints and Metric Learning in Semi-Supervised Clustering

Integrating Constraints and Metric Learning in Semi-Supervised Clustering

Integrating Constraints and Metric Learning in Semi-Supervised Clustering. Mikhail Bilenko, Sugato Basu, Raymond J. Mooney ICML 2004 Presented by Xin Li. Semi-Supervised Clustering. K=4. Semi-Supervised Clustering. Semi-Supervised Clustering. How to exploit supervision in clustering.

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383 views • 21 slides
Three examples of the EM algorithm

Three examples of the EM algorithm

Three examples of the EM algorithm. Week 12, Lecture 1 Statistics 246, Spring 2002.

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256 views • 19 slides

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